在基于启发式优化算法的云环境中进行最佳的强大配置
Jiaxin Zhou1, Siyi Chen1, Haiyang Kuang1
1School of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan Province, China.
PeerJ. Computer science
|December 16, 2024
概括
本研究介绍了云计算系统的新型稳定性策略,以防止由于不可预测的干扰而导致的性能下降. 它通过基于定义的可接受利和等待时间限制来配置服务器大小和速度来确保可接受的系统性能.
科学领域:
- 云计算性能分析 性能分析
- 系统稳固性工程 系统稳固性工程
- 启发式优化算法 启发式优化算法
背景情况:
- 云计算系统容易受到不可预测的干扰,导致性能下降.
- 现有的研究往往优先考虑利最大化或等待时间最小化,忽视性能退化.
- 定义可接受的性能值对于保持系统稳定性至关重要.
研究的目的:
- 量化扰动对云计算性能的影响.
- 为服务器大小和速度开发一个强大的配置策略.
- 引入一种测量系统抗扰强度的方法.
主要方法:
- 根据最低可接受的利和最大可接受的等待时间来定义一个可行的区域.
- 利用强度的概念来指导服务器配置.
- 建议和评估用于强度测量的启发式优化算法.
主要成果:
- 提出的启发式优化算法显示了高精度.
- 与基准方案相比,该算法的解决方案大小误差大约为10^-6.
- 该策略有效地在干扰条件下保持云系统性能在可接受的水平.
结论:
- 开发的稳定性战略有效地减轻了云计算中的性能下降.
- 拟议的启发式优化算法提供了一个精确的强度测量方法.
- 这种方法为配置云系统以应对干扰提供了可靠的框架.
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